The history attention mechanism (HAM) [1]} is among a very few methods that take a model-based approach to selecting history on-the-fly based on the input question.
HAM is motivated by the observation that previous dialog turns in a dialog session are of different importance for answering the input question, and the importance does not necessarily depend on their relative position. As illustrated in the example in Figure REF , while Q2, Q4, Q5 and Q6 are closely related to their immediate previous turns, Q7 is related to a remote question Q1, and Q3 does not follow Q2 but shifts to a new topic.
The HAM module computes for each turn in dialog history a weight based on how useful it is for answering the input question. Then, the history used for generating the answer to the input question is represented as a weighted sum of the vector representations of previous dialog turns.
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